25 citations · 40 across the 6 of their papers we have counts for
6 papers
On Monotonic Aggregation for Open-domain QA
Sang-eun Han, Yeonseok Jeong, Seung-won Hwang +1
Question answering (QA) is a critical task for speech-based retrieval from knowledge sources, by sifting only the answers without requiring to read supporting documents. Specifical…
On Hate Scaling Laws For Data-Swamps
Abeba Birhane, Vinay Prabhu, Sang Han +1
`Scale the model, scale the data, scale the GPU-farms' is the reigning sentiment in the world of generative AI today. While model scaling has been extensively studied, data scaling…
When to Read Documents or QA History: On Unified and Selective Open-domain QA
Kyungjae Lee, Sang-eun Han, Seung-won Hwang +1
This paper studies the problem of open-domain question answering, with the aim of answering a diverse range of questions leveraging knowledge resources. Two types of sources, QA-pa…
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann +122
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained…
Robustifying Multi-hop QA through Pseudo-Evidentiality Training
Kyungjae Lee, Seung-won Hwang, Sang-eun Han +1
This paper studies the bias problem of multi-hop question answering models, of answering correctly without correct reasoning. One way to robustify these models is by supervising to…
Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classification
Vinay Uday Prabhu, Sanghyun Han, Dian Ang Yap +3
In this paper, we propose a Seed-Augment-Train/Transfer (SAT) framework that contains a synthetic seed image dataset generation procedure for languages with different numeral syste…